In order to capture the record of individuals for according them a unique identification, all the necessary information needs to be gathered and managed in an appropriate database. This information includes their name, gender, age, marital status, any photograph and biometric characteristics like fingerprints, palm prints, retinal identification, iris scan, face recognition or speech samples. Such a valuable piece of information is stored at an appropriate database for further identification of individuals and their gender verification.
However, it has been observed that in many instances the gender or the age of individuals is wrongly entered in such databases when the record comparisons are made in real time. This in turn necessitates the requirement of strategy or methods for gender verification, their ethnicity and age estimation from the gathered demographic information. Automated verification of demographic information has numerous applications including passive surveillance such that each individual is correctly identified and his/her identity is stored in a database to be searched whenever the access is sought.
As a result, an active area of research and development is dedicated to improve biometric characteristic identification in recent years. For example, face detection has been a well researched field to detect the gender based on global features (shape, hair contour) and geometric features (eyebrow thickness, nose width etc.) but the accuracy drawn in such cases has been in the range of 85% to 92%.
Another popular approach to estimate gender and age based on formant/pitch analysis is through the use of speech recognition technology. However, current speech recognition based identification typically exhibits high error rates; their accuracy reported as 98% for clean speech and 95% for noisy speech. Further, speech recognition systems work well under laboratory conditions, but intend to show a considerable decrease in recognition rates when used in a normal operating environment. This decrease in accuracy occurs for the most part because of the unpredictable and variable noise levels found in a normal operating setting, and the way individuals alter their speech patterns to compensate for this noise.
Incorporating name as one of the parameters for gender and/or age identification and verification, also poses multiple challenges based on individual geographical origin or location and hence prone to an error attack of approximately 5%.
There is thus a widely recognized need for, and it would be highly advantageous to have, a method and apparatus for automatically reporting error based on an individual's wrong classification with respect to a particular category, such as an age and/or gender-category.
This in turn triggers the need to develop a more mature and reliable system which reports gender verification and consistency of demographic data maintained at the appropriate database by way of extracting the intelligent information using the multiple data inputs instead of only relying on any of the biometric characteristic recognition techniques.